Structural Optimization of Rotating Disk Using Response Surface Equation and Genetic Algorithm

Mohan, S. C.; Maiti, Dipak Kumar

doi:10.1080/15502287.2012.698712

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…e energy storage flywheel [49] uses a flywheel in highspeed rotating to store kinetic energy and convert it into electrical energy when needed. Given a constant speed, the maximum energy storage is proportional to the moment of inertia of flywheel.…”

Section: Engineering Applicationmentioning

confidence: 99%

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Ding

Zheng

2020

Mathematical Problems in Engineering

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Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

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Section: Engineering Applicationmentioning

confidence: 99%

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Ding

Zheng

2020

Mathematical Problems in Engineering

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“…Three parameters define performance characteristics of the disk: the mass of the disk, the maximal radial displacement u max , the maximal stress s max [5,47,9]. It is easy to calculate mass of the disk, as we know all geometrical parameters of the disk, while surrogate modeling of the maximal radial displacement and the maximal stress is needed since these characteristics are computationally expensive [47,41]. So the focus here is on modeling the maximal radial displacement and the maximal stress.…”

Section: Rotating Disk Model Descriptionmentioning

confidence: 99%

Large scale variable fidelity surrogate modeling

Zaytsev

Burnaev

2017

Ann Math Artif Intell

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Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space. Thanks to Gaussian process properties we can use both samples generated by a high fidelity function (an expensive and accurate representation of a physical phenomenon) and a low fidelity function (a cheap and coarse approximation of the same physical phenomenon) while constructing a surrogate model. However, if samples sizes are more than few thousands of points, computational costs of the Gaussian process regression become prohibitive both in case of learning and in case of prediction calculation. We propose two approaches to circumvent this computational burden: one approach is based on the Nyström approximation of sample covariance matrices and another is based on an intelligent usage of a blackbox that can evaluate a low fidelity function on the fly at any point of a design space. We examine performance of the proposed approaches using a number of artificial and real problems, including engineering optimization of a rotating disk shape.

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